Neuroimaging database systems and methods

ABSTRACT

Systems for and methods of utilizing a neuroimaging database are presented. The systems and methods include techniques for analyzing the pathophysiological basis of a chronic brain disease and/or the effectiveness of a treatment for a chronic brain disease, obtaining data for research of a chronic brain disease, searching for chronic brain disease symptoms identified in a clinical patient, searching a database by comparing the brain scan images of patients with suspected indications of chronic brain disease with other patients in the database to identify sets of patients with similar indications in their brain scan images, displaying brain scan information regarding a person, and using image pattern matching to analyze the pathophysiological basis of a chronic brain disease and/or the effectiveness of a proposed or previously administered treatment for a chronic brain disease.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a Continuation of U.S. patent applicationSer. No. 13/298,635 filed Nov. 17, 2011, which is hereby incorporated byreference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates to systems and methods of utilizingneuroimaging databases.

BACKGROUND OF THE INVENTION

Neuroimaging studies of a patient's brain, such as those obtained usingsingle Photon Emission Computed Tomography (SPECT) or FunctionalMagnetic Resonance Imaging (fMRI), are often used in the diagnosis,treatment, and study of chronic brain diseases. As just some examples, aphysician may perform a brain scan to determine a diagnosis for apatient that suffered a serious head injury, to prepare a treatment planfor a patient with Alzheimer's disease, or to assess whether aparticular pharmaceutical is helping a patient who has bipolar disorder.

Clinical Decision Support Systems (CDSS) have become increasinglypopular among physicians and other health care professionals recently.In general, CDSS may refer to computer hardware, software, and/orsystems that can be used to provide clinicians, staff, patients, orother individuals with knowledge and person-specific information,intelligently filtered or presented at appropriate times, to enhancehealth and health care. For example, a physician may use CDSS todetermine a diagnosis for a patient who has certain symptoms. CDSS ofteninclude at least three component parts: a knowledge basis, an inferenceengine, and a communication mechanism. The knowledge base may comprisecompiled information about symptoms, pharmaceuticals, and other medicalinformation. The inference engine may comprise formulas, algorithms,etc. for combining information in the knowledge base with actual patientdata. The communication mechanism may be ways to input patient data andto output helpful information based on the knowledge base and inferenceengine. For example, information may be inputted by a physician using acomputer keyboard or tablet and displayed to the physician on a computermonitor or portable device.

SUMMARY OF THE INVENTION

Various exemplary embodiments provide for ways of utilizing neuroimagingdatabases.

Brain scans may provide a wealth of information regarding the variousportions of the brain down to a very fine detail. When diagnosing,treating, and studying chronic brain diseases, it may be desirable tocompare in various ways a patient's brain scan(s) with those of otherpatients, including those considered normal, those with similarconditions, and those receiving similar treatments, for example. It mayalso be desirable to compare functional images of a patient's brainwhile the patient is at rest, versus while the patient is concentratingand his or her brain is activated.

In one illustrative example, a system and method may be provided foranalyzing the pathophysiological basis of a chronic brain disease and/orthe effectiveness of a certain treatment for a chronic brain disease. Adatabase may contain a set of records for patients that receivedparticular treatments for a chronic brain disease, as well as a set ofrecords for patients that did not receive the treatments. A normativedatabase may contain information, such as brain scans, for patients withnormal brain scans. Through the analysis of statistical comparisons of(1) the treatment set to the normative set, and (2) the non-treatmentset to the normative set, it may be possible to learn more about thechronic brain disease and/or analyze the effectiveness of the treatment.

In another illustrative example, pattern matching may be used. Adatabase may contain perfusion pattern index (PPI) files for individualpatients, where a PPI file may show statistical deviations of the brainperfusion levels in the regions of a patient's brain from a set ofnormative values for patients with no indications of chronic braindisease. One patient's PPI file may be compared to other patients toobtain a set of similar patients, an average PPI file may be determinedfor the set, and the PPI file of each patient may then be compared tothe average PPI file. Such comparison information may also be useful tolearn more about a chronic brain disease and/or analyze theeffectiveness of a treatment.

Other embodiments are also within the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention, together with further objects and advantages, maybest be understood by reference to the following description taken inconjunction with the accompanying drawings, in the several figures ofwhich like reference numerals identify like elements, and in which:

FIG. 1 is a schematic diagram illustrating a system according to anembodiment of the present invention;

FIG. 2 is a portion of an example assessment report according to anembodiment of the present invention;

FIG. 3 is a portion of an example assessment report according to anembodiment of the present invention;

FIG. 4 is a portion of an example assessment report according to anembodiment of the present invention;

FIG. 5A is a detail of a brain scan according to an embodiment of thepresent invention;

FIG. 5B is a detail of a prior art brain scan;

FIG. 6 is an example comparison of a normal brain scan versus an anxietydisorder brain scan;

FIG. 7 is an example comparison of a normal brain scan versus anAlzheimer's disease brain scan;

FIG. 8 is a general information and history page according to anembodiment of the present invention;

FIG. 9 is a scan encounter page according to an embodiment of thepresent invention;

FIG. 10 is a flow chart according to an embodiment of the presentinvention;

FIG. 11 is a flow chart according to an embodiment of the presentinvention;

FIG. 12 is a flow chart according to an embodiment of the presentinvention;

FIG. 13 is a flow chart according to an embodiment of the presentinvention;

FIG. 14 is a flow chart according to an embodiment of the presentinvention;

FIGS. 15A-D are a table for use in an embodiment of the presentinvention; and

FIG. 16 is a portion of an example patient analysis report according toan embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following description is intended to convey a thorough understandingof the embodiments described by providing a number of specificembodiments and details involving systems and methods of utilizing brainscan databases. It should be appreciated, however, that the presentdisclosure is not limited to these specific embodiments and details,which are exemplary only. It is further understood that one possessingordinary skill in the art, in light of known systems and methods, wouldappreciate the use of the invention for its intended purposes andbenefits in any number of alternative embodiments, depending on specificdesign and other needs.

Disclosed herein are systems and methods of utilizing brain scandatabases. According to certain embodiments, techniques for analyzingthe pathophysiological basis of chronic brain diseases and/or theeffectiveness of a proposed or previously administered treatment for achronic brain disease are disclosed. According to other embodiments,techniques for obtaining data for research of a chronic brain diseaseare disclosed. According to other embodiments, techniques for searchingfor chronic brain disease symptoms identified in a clinical patient aredisclosed. According to yet other embodiments, techniques for displayingbrain scan information regarding a person are disclosed. According toyet other embodiments, techniques for using pattern matching to analyzethe pathophysiological basis of a chronic brain disease and/or theeffectiveness of a proposed or previously administered treatment for achronic brain disease are disclosed.

FIG. 1 is a schematic diagram illustrating a system according to anembodiment of the present invention. In particular, FIG. 1 illustrates adatabase 100, which includes a plurality of electronically-storedrecords (e.g., 1000, 2500, 5000, 7500, 10,000, or more). As discussed indetail below, each record corresponds to a person and contains detailedinformation regarding that person.

Each record in database 100 includes at least one, and typically more,functional neuroimaging brain scans. For example, such brain scans maybe obtained using Single Photon Emission Computed Tomography (SPECT)cameras. In this embodiment, a radiopharmaceutical, such at GEHealthcare's Ceretec®, containing a small amount of a radioactiveisotope tracer may be injected into the patient's arm. Theradiopharmaceutical is carried by the blood to the patient's brain,crosses the blood-brain barrier, and is diffused into various regionsthroughout the brain. The tracer emits gamma radiation, which passes outof the patient's head and is captured by one or more detectors in theSPECT camera. The number of gamma rays counted by the camera may bedirectly indicative of the intensity of the tracer uptake and neuronalfunction in various regions of the brain. The resulting brain scans maybe stored as data structures that define a set of numericrepresentations of three dimensional voxels (i.e., volumetric pixels)where the x, y, and z axis represent a specific volumetric area of thebrain. The camera and its software may translate these three dimensionalvoxels into a set of transverse planes where each plane is atwo-dimensional matrix reflecting a particular slice or section of thepatient's brain. The intensity of the image in each cell in the matrixmay represent the level of uptake of the tracer in that part of thebrain. Using Digital Imaging and Communications in Medicine (DICOM)image readers available from various sources (e.g., Siemens eSoft), eachmatrix may be viewed in grey scale or rendered into multicolored imageswhere different levels of intensity are represented by different colors.In various exemplary embodiments, each stored scan may contain datarepresenting several optional scan settings, such as thirty-two planes(or slices), each plane consisting of 64×64 voxels; or sixty-four planes(or slices), each plane consisting of 128×128 voxels; or 128 planes (orslices), each plane consisting of 256×256 voxels. Values for each voxelmay be stored in database 100, and each value may represent data fromthe raw images coming directly from the camera, or the data may reflecta difference from norm. That is, database 100 may store a set of valuesrepresenting brain scan voxels, where each value represents a delta fromwhat a normal brain scan should be for that voxel, as determined bycomparison with one or more normative databases.

Scans may be obtained with a variety of medical imaging devices, suchas, for example, a Siemens E-Cam SPECT camera with low-energy, highresolution (LEHR) parallel hole collimation. In an exemplary embodiment,counts may be collected in a 64×64 matrix with 32 stops of three degreeseach. Total counts may exceed five million. Data may be zoomed to 1.78,corrected for motion artifact, and filtered using a Butterworth filterat 0.60 with an order of six. Attenuation correction may be performed.The volume may be masked to exclude non-neural structures. There may beno post filtering, or post filtering may be performed. Data may bepresented and stored in horizontal, sagittal, and frontal views with,for example, four millimeter sections. Statistical parametric analysismay be performed using various image interpretation software packages,such as, for example, Segami Corporation NeuroSPECT software relative toa normative database containing information for any number ofindividuals (e.g., 64). A computer-implemented tomographicreconstruction algorithm may be applied to the multiple projectionsobtained from the scan, yielding a three dimensional dataset, which maythen be manipulated to show thin slices along any chosen axis. Again,while the previous example described a particular type of SPECTfunctional brain scan, any type of neuroimaging study, and anycombination of neuroimaging modalities (e.g., PET scan and fMRI scan),may be used to populate the database.

Each brain scan may undergo a spatial normalization process for storagein database 100. Such a process may include any, or a combination, oftranslation, rotation, scaling, and nonlinear warping of the brainsurface. Spatial normalization allows for the gathered data to conformto a standard template. Exemplary templates include Talairach-Tournouxand those available from the Montreal Neurological Institute.

Data from each scan may be stored using, by way of non-limiting example,Cartesian coordinates or Talairach coordinates. An exemplarynon-limiting example image set may include raw DICOM files showing scansof the patient's brain in multiple planes, one or more sets of processedDICOM images (for detailed resolution and clarity), and focused imagesets in PNG or JPG formats, highlighting parts of the patient's brainthat show specific functional abnormalities. (The DICOM standard is alsoknown as NEMA standard PS3 and ISO standard 12052:2006.)

Each record in database 100 may include a brain scan representing anat-rest state, also known as a baseline state, which may be gathered asfollows. The subject may be placed in a comfortable reclining chair, andan intravenous line may be started. The subject may then be allowed toacclimate to a quiet semi-darkened room with sound-dampening headphonesin place, according to established practice guidelines. A 99mTc-labeledTc-Hexamethylpropyleneamine Oxime (HMPAO, also known as exametazime)tracer may then be injected through the intravenous line, and theintravenous line may be flushed with saline. Other types ofradiopharmaceutical tracers may be used as well (e.g., 123I IMP tracer,99mTc ECD tracer). The perfusion pattern of the subject's brain may thenbecome fixed physiologically in the patient's brain during thesubsequent three minutes. After injection, the subject may remain in thequiet semi-darkened room for an additional period. Scans may be acquiredforty minutes after tracer injection.

Each record in database 100 may include a functional brain scanrepresenting an active state, which may be gathered as follows. For aconcentration task, the subject may be placed in a quiet room and anintravenous line may be started. The subject may then perform aconcentration test, such as, by way of non-limiting example, a Stroopcolored word test on a laptop computer. Approximately five minutes intothe concentration test, the 99mTc-labeled HMPAO tracer may be injectedthrough the intravenous line, and the intravenous line may be flushedwith saline. The subject may complete the concentration test, and fortyminutes after injection, the patient may be scanned.

Brain scans in database 100 may be obtained from patients or othersubjects, obtained from third parties (e.g., scanned by businesspartners, purchased from vendors), or a combination thereof.

Each record in database 100 further may include detailed data on any, ora combination, of a clinical history, a family history, presentingsymptoms, a doctor's diagnosis, medications, previous treatments, areferring physician's reasons for ordering the scan, military service,and any current and previous substance or alcohol abuse history. Theclinical history may contain information on any, or a combination, ofdevelopmental history (including information about childhood andadolescent developmental traumas), specific brain trauma (e.g., fromaccidents, sports, toxic exposure), surgeries and hospitalizations,other imaging procedures completed (including copies of such images),medical and environmental allergies, and family members' conditions,diseases, and disorders. Each record may further include a writtenreport correlating specific scan images with potential conditions anddisorders. Each record may further include one or more structuredneuropsychiatric inventories, such as, by way of non-limiting example, aMini International Neuropsychiatric Interview (MINI) and a MontrealCognitive Assessment (MoCA). Each record may include a report by one ormore radiologists or physicians trained to interpret the brain scansindicating the radiologists' and/or physicians' findings,interpretations, and recommendations. The data referred to in thisparagraph may be stored in any of a variety of formats such as, by wayof non-limiting example, ASCII, MICROSOFT WORD, and Portable DocumentFormat (PDF), or contained within the data fields of software programssuch as Microsoft Customer Relationship Management (CRM).

Collectively, the data in a record in database 100 for a particularpatient that may reflect or refer to the patient's medical state at anytime may be referred to as “patient medical information.” Patientmedical information may include, by way of non-limiting example,clinical history, family history, presenting symptoms, a doctor'sdiagnosis, medications, previous treatments, military service, and anycurrent and previous substance or alcohol abuse history.

In certain embodiments, database 100 may include records for patientsdiagnosed with, by way of non-limiting example, traumatic brain injury,Alzheimer's disease, dementia, bipolar disorder, attention deficithyperactivity disorder (ADHD), anxiety, autism and other brainconditions and disorders.

Database 100 may be implemented in various ways. By way of non-limitingexample, database 100 may be built on a Structured Query Language (SQL)platform and accessed through a Microsoft Dynamics Customer RelationshipManagement (CRM) front-end. The CRM may be structured as an electronichealth record (EHR), electronic medical record (EMR), clinical datarepository, or patient registry. Database 100 may capitalize on theinherent capabilities of the CRM platform to organize and automate manycorporate and clinical processes into an integrated, computer-aidedmanagement system. This may include marketing and sales functionsrelated to acquiring new referring attorneys and physicians, as well astracking patient acquisition and booking. It may also integrate billingand accounting functions, such as by tracking the progress of patientsthrough various steps and capturing cost and revenue data.

Database 100 (and associated systems and infrastructure) may beconfigured to comply with U.S. and international standards, such asHL-7, ISO TC/251 and DICOM, European standards such as CEN, and theapplicable U.S. regulations for Health Insurance Portability andAccountability (HIPAA), 21 C.F.R. Part 11, Health Information Technologyfor Economic and Clinical Health (HITECH), and similar statutes. Datamay be stored in unstructured (e.g., natural language) form or encodedusing standard healthcare industry standards such as ICD-9/10,SNOMED-CT, RxNorm, or LOINC. Data entry may be performed using a varietyof techniques, such as direct entry by keyboard, tablet or touchpad,screen capture, speech recognition, file transfers, or imported fromother systems and software.

Database 100 may be accessed by various entities. Examples of suchentities include patients, physicians, research organizations, imagingcompanies, and attorneys involved in brain injury cases. Such entitiesmay retrieve data from the database, and, in some cases, add data to thedatabase. Access to database 100 may be provided over secure, high-speedInternet connections using, by way of non-limiting example, highperformance WAN optimization software licensed from CircadenceCorporation. The DICOM communications protocol may be used.

The database and access arrangements may be implemented usingappropriate privacy and security measures, such as user authenticationand authorization, passwords, data-de-identification, encryption, andaccess control methodologies to insure compliance with all federal andstate requirements. Servers and access control devices may be maintainedin secure computing and hosting facilities to assure compliance withHIPAA and other laws and regulations.

Certain embodiments of the present invention may also include, orinclude access to, a normative database. The normative database may be aportion of database 100. Alternately, or in addition, the normativedatabase may be a physically or logically separate database. Thenormative database may include data associated with patients with normalbrain scans. Thus, the normative database may include records of aplurality of clinical patients. Each record may include at least one,and potentially more, brain scans. Both baseline and concentration brainscans may be included. FIG. 2 is a portion of an example assessmentreport according to an embodiment of the present invention. Inparticular, FIG. 2 illustrates a three-dimensional Talairach renderingof brain activity. The brain scan data reflected in the image of FIG. 2is spatially normalized such that it is presented on a standard image ofa generic brain. The activity depicted represents deviations from normalfor each surface voxel. Because the brain under analysis in FIG. 2 isthat of an elderly patient, deviations may be judged with a normalelderly brain activity as a baseline represented by the color grey. Eachcolor in the image of FIG. 2 represents a departure from such a baselinestate.

In general, brain scan data may be presented in visual form in reportsaccording to embodiments of the present invention according to any ofvarious techniques. Colors may be used with any of these techniques todepict deviations from normal brain activity. Two-dimensional slices ofdata representing a three-dimensional brain scan may be utilized (FIGS.4-6). For such slicing techniques, planes may be in any configuration ororientation (e.g., sagittal, coronal, transverse, curved plane).Isosurface rendering may be employed. For volume rendering techniques,direct volume rendering may be implemented (e.g., multi-threshold directvolume rendering). Opacity, color, refractive index, and orientation maybe adjusted by users in real time. Other techniques include maximalintensity projections and shaded surface displays (e.g., multi-thresholdshaded surface displays). Mesh or grid computational expedients may beemployed with any of the aforementioned techniques.

FIG. 3 is a portion of an example assessment report according to anembodiment of the present invention. In particular, FIG. 3 depictsmulti-threshold volume rendering of brain activity. Both baseline andtest (concentration) brain activity are represented. Thresholds of 60%and 85% are depicted in FIG. 3.

FIG. 4 is a portion of an example assessment report according to anembodiment of the present invention. In particular, FIG. 4 depictssagittal two-dimensional slices of the three-dimensional brain scandata. Activity on both sides of the brain is depicted, where differentcolors represent deviations from normal activity.

FIG. 5A is a detail of a brain scan according to an embodiment of thepresent invention. In particular, FIG. 5A depicts a transversetwo-dimensional slice of three-dimensional brain scan data. Theresolution of the data depicted in FIG. 5A is 64×64 voxels, with a totalof 32 planes, of which the plane in FIG. 5A is one.

FIG. 5B is a detail of a prior art brain scan. Prior art brain scansinclude those of only 32×32×32 voxels. The scan depicted in FIG. 5B isnot as detailed as the scan depicted in FIG. 5A.

FIG. 6 is an example comparison of a normal brain scan to an anxietydisorder brain scan. In particular, FIG. 6 depicts transversetwo-dimensional slices of three-dimensional brain scan data for a normalbrain and a brain of a patient diagnosed with anxiety disorder.Embodiments of the present invention are capable of simultaneouslypresenting any two comparable (e.g., depicting the same general brainregion in the same way) brain images. Such images may be presented on acomputer screen. Certain embodiments are capable of generating an imagefile (e.g., GIF, JPEG, PNG, etc.) containing two or more images, withany (or no) associated written data. Such an image file may be exportedfrom the generating computer system and utilized in a report,demonstration, or other presentation.

FIG. 7 is an example comparison of a normal brain scan versus anAlzheimer's disease brain scan. In particular, FIG. 7 depicts aTalairach rendering of brain scan data for a normal brain and a brain ofa patient diagnosed with Alzheimer's disease. As discussed above inrelation to FIG. 6, embodiments of the present invention are capable ofsimultaneously presenting any two comparable brain images.

FIG. 8 is a general information and history page according to anembodiment of the present invention. The page may be part of an overalluser interface to the database of brain scan information contemplatedaccording to various embodiments of the present invention. Users of thedatabase may enter patient information in an interface page, such asthat depicted in FIG. 8, which is displayed on a computer using computersoftware. That is, FIG. 8 depicts that patient data (e.g., patientdemographic data) may be gathered and stored in the overall database.Such information includes, by way of non-limiting example, patient name,gender, address, telephone numbers, and date of birth. The database mayalso include digital representations of consent forms as executed bypatients. Such consent forms may include language providing that patientdata may be added to the database and accessed in a manner so as tocomply with relevant laws and regulations. For example, the consentforms may specify that patient brain scan data and associated diagnoseswill be made available to database users in a manner that does notdivulge any personally identifying information.

FIG. 9 is a scan encounter page according to an embodiment of thepresent invention. Like the page depicted in FIG. 8, the page of FIG. 9may be part of an overall user interface to the database of brain scaninformation contemplated according to various embodiments of the presentinvention. Brain scan technologists or other database users may enterinformation relating to brain scan events in an interface page, such asthat depicted in FIG. 9. Such data may be gathered and stored in theoverall database. Information entered in the interface depicted in FIG.9 may include, by way of non-limiting example, patient name, scan eventdate and time, identification of the scanning machine, a patientclassification, a patient age at the scan event, presenting symptoms,other symptoms, an incoming diagnosis, other diagnoses, a reason for theassociated scan, a referring physician, an indication for the referral,and demographic information of the referring physician(s). Thepresenting symptoms may be selected from a list of available symptoms,such as that available from a pre-populated drop-down menu. Multiplescan encounters may be entered in the page depicted in FIG. 9.

FIGS. 10-14 are flow charts according to various exemplary embodimentsof the present invention. The methods depicted in FIGS. 10-14 may beimplemented using, for example, what is commonly referred to in the artas Clinical Decision Support Systems (CDSS).

FIG. 10 is a flow chart according to an embodiment of the presentinvention. Specifically, FIG. 10 illustrates a method of analyzing thepathophysiological basis of a chronic brain disease and/or theeffectiveness of a certain treatment for the chronic brain disease. Oncea chronic brain disease and putative treatment are identified, theprocess begins at block 1005, where a database may be provided. Thedatabase may include electronically stored brain scans of patientsdiagnosed with the chronic brain disease at issue. An exemplary suchdatabase is discussed in detail above with respect to FIG. 1.

At block 1010, the method identifies a treatment set of patient recordsin the provided database. The treatment set of patient recordscorresponds to patients that have been diagnosed with the chronic braindisease at issue and have received the putative treatment. In someembodiments, the step portrayed at block 1010 may be broken down intosub-steps as follows. A database user may generate an input to thedatabase that is intended to identify the treatment set. Such an inputmay include a statement formatted in a language particular to databases,such as, by way of non-limiting example, in SQL. The statement may becompiled by the database into an executable query, which is thenexecuted by the database. Such execution may reveal the patient recordshaving the properties set forth in the database user's input, that is,the treatment set.

At block 1015, the method identifies a non-treatment set of patientrecords in the provided database. The non-treatment set of patientrecords corresponds to patient that have been diagnosed with the chronicbrain disease but who have not received the putative treatment. In someembodiments, the step associated with block 1015 may be broken down intosub-steps. A database user may generate an input (e.g., a SQL statement)to the database that is intended to identify the non-treatment set. Theinput may be compiled by the database into an executable query, which isthen executed by the database. Such execution may reveal the patientrecords having the properties set forth in the database user's input. Inparticular, the execution may identify a non-treatment set of patientrecords.

At block 1020, the method accesses a normative database. The normativedatabase may be as discussed above in reference to FIG. 1. Thus, thenormative database may include brain scans and/or other informationassociated with patients that have normal brain scans. Access may beover an electronic connection over one or more computer networks, suchas the Internet. Alternately, or in addition, access may occur due toownership of the normative database by the same entity that owns orcontrols the database provided at block 1005.

At block 1025, the method outputs statistical comparisons. Specifically,at block 1025, the method outputs a comparison of the treatment set ofpatient records to records in the normative database, and a comparisonof the non-treatment set of patient records to the normative set ofpatient records. The statistical comparisons may include analyses of theaverage or mean variations in the perfusion levels in various regions ofthe brains of patients included in the treatment set versus the same orsimilar regions for patients in the non-treatment set, overall patternsor maps of the measured perfusion levels of patients in either set(treatment and non-treatment), as well as data, charts, and/or diagramscomparing the measured perfusion levels and their variations over timeduring the course of a given treatment program. Additionally,statistical comparisons may include analysis of current or priorsymptoms, treatments, medications, and other non-image clinicalinformation. Output may be in human readable form, e.g., on a computermonitor. Alternately, or in addition, output may be in machine-readableform, such as on a transitory or non-transitory computer readablemedium.

FIG. 11 is a flow chart according to an embodiment of the presentinvention. Specifically, FIG. 11 illustrates a method of obtaining datafor research of a chronic brain disease. Once a chronic brain disease isidentified, the process begins at block 1105, where a database isprovided. The database may include electronically stored brain scans ofpatients diagnosed with the chronic brain disease at issue. An exemplarysuch database is discussed in detail above with respect to FIG. 1.

At block 1110, the method identifies a disease set of patient records inthe provided database. The disease set of patient records may correspondto patients that have been diagnosed with the chronic brain disease atissue. In some embodiments, the step portrayed at block 1110 may bebroken down into sub-steps as follows. A database user may generate aninput (e.g., a SQL statement) to the database that is intended toidentify the disease set of patient records. The input may be compiledby the database into an executable query, which is then executed toreveal the patient records having the properties set forth in thedatabase user's input. In particular, the execution may identify adisease set of patient records.

At block 1115, the method identifies and outputs common features of thedisease set of patient records. In certain embodiments, the method maysearch patient medical information in the disease set of records forfeatures that are common to a high percentage of the records. By way ofnon-limiting example, such features may include traumatic brain injury,exposure to toxic chemicals, oxygen deprivation, drug use, etc. Athreshold percentage may be set by a user of the embodiments such thatonly features that are present in at least that percentage of recordsare identified. Exemplary percentages include, by way of non-limitingexample, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90%. In some embodiments,at block 1115, the identified common features are output in humanreadable form in a manner that associates each feature with acorresponding percentage of disease records that include the feature inthe associated patient medical information. Such information may bepresented in chart form, as exemplified in a non-limiting fashion below.

TABLE 1 Features Identified in Disease Set of Records Feature PercentageGender = Male 93% Past Trauma = Traumatic Brain Injury 47% Number withloss of consciousness 42%

At block 1120, the method accesses a normative database. The normativedatabase may be as discussed above in reference to FIG. 1, includingbrain scans showing, for example, the normal perfusion patterns for thevarious regions of the brain for patients that have normal brain scans.Access may be over an electronic connection over one or more computernetworks, such as the Internet. Alternately, or in addition, access mayoccur due to ownership of the normative database by the same entity thatowns or controls the database provided at block 1105.

At block 1125, the method compares a brain scan of a patient in thedisease set to corresponding normative brain scans to obtain statisticalinformation. Such information may include, for example, measurements ofthe deviations (positive or negative) of one or a plurality of regionsof a brain for a patient in the disease set as compared with the averageor mean values for corresponding regions in the images in the normativedatabase. In various exemplary embodiments, this information may bepresented in a table as shown in FIG. 16. Using this data, the user maymake an assessment of the level or degree of functionality in variousparts or subparts of a patient's brain and then use that assessment toform a diagnosis of the causes or implications of evident injuries orabnormalities revealed in the patient's brain scans. Alternatively, thedata may be presented in a summary form similar to that in Table 1.

FIG. 12 is a flow chart according to an embodiment of the presentinvention. Specifically, FIG. 12 illustrates a method of searching forsymptoms identified in a clinical patient. Once symptoms of a clinicalpatient are obtained by evaluation, referral, or other technique, theprocess begins at block 1205, where a database is provided. The databasemay include electronically stored brain scans of patients diagnosed withthe chronic brain disease at issue. An exemplary such database isdiscussed in detail above with respect to FIG. 1.

At block 1210, the method identifies a candidate set of patient recordsin the provided database. The candidate set of patient recordscorresponds to patients that have similar symptoms to those of theclinical patient listed in the patient medical information of theirassociated records. In some embodiments, the step portrayed at block1210 may be broken down into sub-steps as follows. A database user maygenerate an input (e.g., a SQL statement) to the database that isintended to specify the symptoms of the clinical patient. The input maybe compiled by the database into an executable query, which is thenexecuted to reveal the patient records having the properties set forthin the database user's input. In particular, the execution may identifya candidate set of patient records.

At block 1215, the method ranks the candidate set of records accordingto relevance to the clinical patient's symptoms. Such techniques areknown to those of skill in the art and include, by way of non-limitingexample, keyword weighting based on inverse frequency and statisticalintent. Relevance ranking may be performed using, by way of non-limitingexample, APACHE LUCENE CORE 3.3, available from lucene.apache.org. Thus,block 1215 may generally order the candidate set of patient recordsaccording to relevance.

At block 1220, the method outputs at least a portion of the orderedcandidate set of patient records. The method may output the top few(e.g., 1, 3, 5, 10, 25, 50, 100) records. Output may be in humanreadable form, e.g., on a computer monitor. Alternately, or in addition,output may be in machine-readable form, such as on a transitory ornon-transitory computer readable medium. The output may allow a doctorto evaluate whether the clinical patient suffers from one or moreconditions whose presence is indicated in the ordered segment of thecandidate patient records.

Some embodiments of the invention related to the embodiments of FIG. 12allow a user to easily compare two or more brain scans. Such relatedembodiments may display two or more brain scan images sequentially inthe same physical space. That is, such embodiments may display a firstbrain scan on a dynamic medium such as a computer monitor. Theembodiments may then display a second scan in the same place as thefirst scan after removing the image of the first scan. Some embodimentsallow a user to manually switch back and forth between images, e.g., bytouching a key on a computer keyboard. Some embodiments allow a computerto automatically flip between images, e.g., at an adjustable rate (byway of non-limiting example, 0.25 hertz, 0.5 hertz, 1 hertz). Someembodiments allow for more than two such images. Advantages of theembodiments discussed in this paragraph include methods based onso-called “blink comparators” that can be used to easily detectdifferences between the displayed brain scans where differences will“blink” or “flicker” upon rapidly switching the images.

FIG. 13 is a flow chart according to an embodiment of the presentinvention. Specifically, FIG. 13 illustrates a method of displayingbrain scan information regarding a person. Once the person isidentified, the process begins at block 1305, where a database isprovided. The database may include electronically stored brain scans ofpatients diagnosed with the chronic brain disease at issue. An exemplarysuch database is discussed in detail above with respect to FIG. 1.

At block 1310, the method obtains at least two brain scans of theperson. In particular, the method may obtain a baseline brain scan and aconcentration brain scan. Both types of scans are discussed in detailabove in reference to FIG. 1. In various exemplary embodiments, atechnologist may obtain the scans using a SPECT scanner as discussedabove.

At block 1315, the method identifies a candidate set of patient recordsin the provided database. The candidate set of patient recordscorresponds to patients that have similar symptoms to those of theperson. In some embodiments, this step may be broken down intosub-steps. A database user may generate an input (e.g., a SQL statement)to the database that is intended to specify the symptoms of the person.The input may be compiled by the database into an executable query,which is then executed to reveal the patient records having theproperties set forth in the database user's input. In particular, theexecution may identify a candidate set of patient records.

At block 1320, the method displays both a brain scan of the person and abrain scan obtained at step 1315. Such display may be effectuated on, byway of non-limiting example, a computer monitor, a projection device, aprinted poster, a plasma television set, a LCD television set, or ahandheld device, such as a smart phone, tablet, personal digitalassistant (PDA), or similar device. In some embodiments, the two brainscans are displayed side-by-side. In other embodiments, the two brainscans are displayed in a partially or completely overlapping manner,where at least one of the scans is at least partially transparent.

Embodiments of the invention discussed in reference to FIG. 13 may alsobe used, for example, to generate demonstrative exhibits in a legalproceeding. In such embodiments, the displaying step may includegenerating poster-sized hard copies in full color, or generatingelectronic images suitable for displaying in a large-scale forum, suchas a court room.

FIG. 14 is a flow chart according to an embodiment of the presentinvention. Specifically, FIG. 14 illustrates a method of using patternmatching to analyze the pathophysiological basis of a chronic braindisease and/or the effectiveness of a proposed or previouslyadministered treatment. The process begins at block 1405, where adatabase is provided. The database may include electronically storedbrain scans of patients diagnosed with the chronic brain disease atissue. An exemplary such database is discussed in detail above withrespect to FIG. 1.

At block 1410, the brain scans of a particular patient are selected fromthe provided database. In some embodiments, the step portrayed at block1410 may be broken down into sub-steps as follows. A database user maygenerate an input to the database that is intended to identify theparticular patient. Such an input may include a statement formatted in alanguage particular to databases, such as, by way of non-limitingexample, SQL. The statement may be compiled by the database into anexecutable query, which is then executed by the database. Such executionmay reveal the particular patient's records.

At block 1415, a brain Perfusion Pattern Index (PPI) computer file iscreated, which may contain the results of comparing one or more specifictomographic planes, slices, or segments (e.g., sagittal, coronal,transverse, curved plane) of the patient's brain with the correspondingtomographic planes, slices, or segments of the normative database, withthe results being defined in the form of a two-dimensioned table or athree-dimensioned array. FIGS. 15A-D depict an exemplary embodiment ofsuch a table. The cells in the table or array may contain statisticallyderived deviations between the patient's brain and the normal brain forone or more regions, with the values expressed as standard deviations,arithmetic means, or other mathematical expressions. The cells in thePPI table or array may represent variable areas or regions of interestwithin each plane and define individual voxels or groupings of voxels toencompass specific areas or volumes of the brain topology. The table orarray cells may be identified using a coordinate-based mapping system(e.g., X-Y-Z or radial) or alternatively may be assigned easilyremembered names or alphanumeric identifiers. In various exemplaryembodiments, values (e.g., predetermined integers) representing thedifferences in brain perfusion levels may be used in the calculations.These differences may be mapped to particular regions of interest in thebrain. For example, as shown in FIGS. 15A-D, one comparison maycorrespond to the “Anterior Cerebral-Left” portion of the brain, whileanother may correspond to the “Cerebral Cortex-Left” portion of thebrain. Thus, if a user (e.g., a physician) is interested in a particulararea of the brain, he or she can easily access the data for that area.Also, in various exemplary embodiments, a color display may be generatedshowing the patient's brain scans, the brain scans from the normativedatabase, and/or the comparison data between the two as levels of colorintensity.

An appropriately trained user (e.g., a physician) who has expertise withbrain anatomy and functional neuroscience, for example, may use the datain FIGS. 15A-D to perform a logical analysis of abnormal functions inthe brain in order to formulate a diagnosis and/or recommend treatment.Similarly, one or more computer programs may use data similar to thatportrayed in FIGS. 15A-D to develop mathematical constructions of apatient's brain scan for comparison with similar constructions of otherpatients' brain scans. Referring to FIGS. 15A-D, an example of acomputer instruction might be: “Find all the patients in a disease setin database 100 where the values for ‘Anterior Cerebral-Left’ are lessthan N1 and where the values for ‘Cerebral Cortex-Left’ are less thanN2, then display a list of the patient's medical identification numberson a computer monitor or display.” One or more computer programs may beprovided in the form of a Clinical Decision Support System to assist indiagnosing patients with chronic brain diseases by grouping instructionsets to search the database for patients with similar perfusion patternsand perform various data analyses of their demographics, previous andcurrent symptoms, diagnoses, treatments, and clinical outcomes toprovide insights to aid in clinical diagnoses and treatment evaluations.Such instruction sets may be pre-programmed or made available on an adhoc basis using various input methods (e.g., point and click, drop downmenu, or text entry).

Returning to FIG. 14, at block 1420, the PPI for the patient is storedwith the patient's medical record in the database to serve as amathematical index for subsequent search and comparison operations.

The process illustrated in blocks 1405-1420 may be repeated for one ormore other patients, resulting in a database comprising, among otherthings, multiple PPI files.

It will be recognized by a person of ordinary skill in the art thatvarious commercially available computer software packages and algorithmsmay be used for pattern matching in images, such as the Medical ImageProcessing, Analysis, and Visualization (MIPAV) application, offered bythe National Institutes of Health. Also, significant research has beenpublished regarding the use of Support Vector Machines for Regression(SVR) for performing brain image analysis and pattern recognition. Suchsoftware and algorithms may be used for pattern matching as describedherein. It will also be recognized, however, that image matching may becomputationally intensive and slow. The substitution of a mathematicaltable or array indexing scheme, as described herein, may increase searchspeed and reduce the processing power required to perform patternmatching.

At block 1425, a query is made to a Perfusion Pattern Matching Software(PPMS) rules engine by inputting a specific patient's PPI and specifyinguser-variable search parameters, such as defined coordinates, region(s)of interest, or limits of variability. At block 1430, the PPMS executes,and at block 1435, the PPMS rules engine searches all or a subset ofother patients within the database for those patients whose PPIs matchthe subject patient within the user-selected range. At block 1440, thePPMS outputs for subsequent analysis a list of the patients whose PPIsfall within the search parameters in either human or machine readableformat.

Once patients with similar symptoms, histories, or other criteria areselected from the database, either through the process illustrated inblocks 1425-1440 or by a user inputting a request for a set of patients,the PPMS rules engine at block 1445 uses the set of patient databaseidentifiers (e.g., medical record numbers), along with user-variablesearch parameters and variability limits, to calculate an average ormean PPI (aPPI) for the aggregate group of patients, using standardstatistical methods. At block 1450, the PPMS rules engine scores eachpatient based on their consistency with the aPPI and the user-selectedparameters, and returns the result in human or machine readable format.The aPPI may be retained as a library file in the PPMS for future use insearching the database and/or comparing and scoring additional patients.

FIG. 16 is a portion of an example patient analysis report according toan embodiment of the present invention. Using the methods and systemsdescribed herein, reports may be compiled and presented to the user in,for example, a graphical display. The report depicted in FIG. 16, forexample, shows an analysis of 374 patients who had brain scans in lightof several variables. The report shows the reason for the brain scan,the percentage of the scanned group, the number of patients with ahistory of hypoxia, information regarding the diagnosis of the incomingphysician, and information regarding the findings of the outgoingphysician. The report may include an area where a user, such as Dr.Smith, may input preliminary observations based on the compiled data. Itis to be appreciated that any of the data discussed herein may becompiled and/or displayed in a report of any type, including thatdepicted in FIG. 16.

Though this application generally refers to SPECT scans, any otherneuroimaging study may be implemented in addition or in the alternative,such as Positron Emission Tomography (PET), Functional MagneticResonance Imaging (fMRI), or Diffusion Magnetic Resonance Imaging (e.g.,diffusion weighted imaging or diffusion tensor imaging). Any type ofbrain scan, and any combination of brain scans (e.g., SPECT scan andfMRI scan), may be used.

According to various embodiments, data storage may take any of manydifferent forms. Data may be stored via network accessible storage andmay be local, remote, or a combination thereof. Data storage may utilizea redundant array of inexpensive disks (RAID), tape, disk, a storagearea network (SAN), an interne small computer systems interface (iSCSI)SAN, a Fibre Channel SAN, a common Internet File System (CIFS), networkattached storage (NAS), a network file system (NFS), or other computeraccessible storage.

According to various embodiments, the database may be implemented using,by way of non-limiting example, an Oracle database, a Microsoft SQLServer database, a DB2 database, a MySQL database, a Sybase database, anobject oriented database, a hierarchical database, or other database.

It is to be appreciated that the set of instructions, e.g., thesoftware, that configures the computer operating system to perform theoperations described above may be contained on any of a wide variety ofmedia or medium, as desired. Further, any data that is processed by theset of instructions might also be contained on any of a wide variety ofmedia or medium. That is, the particular medium, that is, the memory inthe processing machine, utilized to hold the set of instructions or thedata used in the invention may take on any of a variety of physicalforms or transmissions, for example. Illustratively, the medium may bein the form of paper, paper transparencies, a compact disk, a DVD, anintegrated circuit, a hard disk, a floppy disk, an optical disk, amagnetic tape, a RAM, a ROM, a PROM, a EPROM, a wire, a cable, a fiber,communications channel, a satellite transmissions or other remotetransmission, as well as any other medium or source of data that may beread by a computer.

It is also to be appreciated that the various components describedherein, such as a computer running executable computer software and adatabase, may be located remotely and may communicate with each othervia electronic transmission over one or more computer networks. Asreferred to herein, a network may include, but is not limited to, a widearea network (WAN), a local area network (LAN), a global network such asthe Internet, a telephone network such as a public switch telephonenetwork, a wireless communication network, a cellular network, anintranet, or the like, or any combination thereof. In various exemplaryembodiments, a network may include one, or any number of the exemplarytypes of networks mentioned above, operating as a stand alone network orin cooperation with each other. Use of the term network herein is notintended to limit the network to a single network.

In the preceding specification, various preferred embodiments have beendescribed with references to the accompanying drawings. It will,however, be evident that various modifications and changes may be madethereto, and additional embodiments may be implemented, withoutdeparting from the broader scope of invention as set forth in the claimsthat follow. The specification and drawings are accordingly to beregarded in an illustrative rather than restrictive sense.

We claim:
 1. A system comprising: an electronic database comprising aplurality of records, each of the plurality of records associated with arespective patient, each of the plurality of records comprising: one ormore digital representations of a baseline functional brain scan imagecomprising a plurality of image planes for a respective patient, adigital representation of a medical history of a respective patient, adigital representation of demographic information of a respectivepatient, and a digital representation of a set of clinical symptoms of arespective patient; and one or more computer processors that: receive afirst input from a database user, convert the first input into a firstquery, and execute the first query, wherein a first subset of theplurality of records is identified, the first subset of the plurality ofrecords corresponding to a treatment set of patients, each of whoserespective database records indicate a diagnosis of a brain conditionand that the respective patient in the treatment set received atreatment, receive a second input from a database user, convert thesecond input into a second query, and execute the second query, whereina second subset of the plurality of database records is identified, thesecond subset of the plurality of database records corresponding to aset of patients that did not receive the treatment, each of whoserespective database records indicate a diagnosis of the brain condition,output a first statistical comparison of the first subset of theplurality of database records to a normative set of electronic recordscorresponding to patients that were not diagnosed with the braincondition and did not receive the treatment; and output, for each of theone or more features, a percentage of the records in the normativedatabase of electronic records in which the one or more features arepresent, wherein the percentage of the records further comprises apercentage of disease database records in which the one or more featuresare present and a percentage of normative database records in which theone or more features are present.
 2. The system of claim 1, wherein theone or more computer processors further: output a second statisticalcomparison of the second subset of the plurality of database records tothe normative set of electronic records, wherein information regardingone or more of the basis of the brain condition and effectiveness of thetreatment for the brain condition are analyzed based at least in part onthe first statistical comparison and the second statistical comparison.3. The system of claim 1, wherein each digital representation of abaseline functional brain scan image comprises a set oflocation-specific values reflecting differences from normal.
 4. Thesystem of claim 1, wherein the one or more computer processors furtheridentify and output one or more features present in at least apercentage of a condition subset of the plurality of records.
 5. Thesystem of claim 1, further comprising a nuclear imaging cameraconfigured to obtain a baseline brain scan of the person comprising aplurality of image planes and a concentration brain scan image of theperson comprising a plurality of image planes.
 6. The system of claim 1,wherein the one or more computer processors further: receive an inputreflecting a set of symptoms of the person, convert the input into afirst query, and execute the first query, wherein a candidate subset ofthe patient database records is identified, the candidate subset of thepatient database records corresponding to a set of patients whoserespective patient database records include the set of symptoms of theperson, and cause the display of: at least one baseline brain scan imageand at least one concentration scan image, and at least one brain scanimage of the candidate subset of the patient database records.
 7. Thesystem of claim 1, wherein one or more computer processors further:receive an input from a database user, wherein the input comprises anidentifier for a first patient having an index file in the plurality ofrecords, and one or more search parameters, wherein the index filecomprises brain measurements compared to norm values, convert the inputinto a query, and execute the query, wherein a subset of the pluralityof records is identified, the subset of the plurality of recordscorresponding to a set of patients, each of whose respective databaserecords indicate an index file that substantially matches the firstpatient's index file based on the one or more search parameters,determine an average index file for the subset of the plurality ofrecords, and output a comparison of the index file of each patient inthe set of patients to the average index file.
 8. The system of claim 7,wherein the index file comprises brain measurements compared to normvalues.
 9. The system of claim 1, wherein the one or more computerprocessors further: create an index file for a first patient having atleast one record in the plurality of records, wherein the index filecomprises brain measurements compared to norm values and wherein theindex file is created at least in part by determining digital levels ofstatistical deviation in brain measurement levels between the brainscans of the first patient and normative brain scans of patients with noindications of chronic or acute brain conditions, store the index filein the plurality of records, execute a query, where a subset of theplurality of records is identified, the subset of the plurality ofrecords corresponding to a set of patients, each of whose respectivedatabase records indicate an index file that substantially matches thefirst patient's index file based on one or more search parameters,determine an average index file for the subset of the plurality ofrecords, and output a comparison of the index file of each patient inthe set of patients to the average index file.
 10. The system of claim9, wherein the index file is a multi-dimensioned table or array.
 11. Thesystem of claim 10, wherein the index file is created using valuesrepresenting differences in brain measurement levels between the brainscans of the first patient and the normative brain scans.
 12. The systemof claim 9, wherein each digital representation of a baseline functionalbrain scan image comprises a set of location-specific values reflectingdifferences from normal.
 13. The system of claim 1, wherein one or morecomputer processors further: receive an input from a database user,wherein the input comprises an identifier for a first patient having anindex file in the plurality of records, and one or more searchparameters wherein the index file comprises brain measurements comparedto norm values.
 14. The system of claim 1, wherein one or more computerprocessors further: create an index file for a first patient having atleast one record in the plurality of records, wherein the index filecomprises brain measurements compared to norm values and wherein theindex file is created by determining digital levels of statisticaldeviation in brain perfusion levels between the brain scans of the firstpatient and normative brain scans of patients with no indications ofchronic brain conditions.
 15. The system of claim 1, wherein eachdigital representation of a baseline functional brain scan imagecomprises a set of location-specific values reflecting differences fromnormal.
 16. A system comprising: an electronic database comprising aplurality of records, each of the plurality of records associated with arespective patient, each of the plurality of records comprising: one ormore digital representations of a baseline functional brain scan imagecomprising a plurality of image planes for a respective patient, adigital representation of a medical history of a respective patient, adigital representation of demographic information of a respectivepatient, and a digital representation of a set of clinical symptoms of arespective patient; and one or more computer processors that: receive afirst input from a database user, convert the first input into a firstquery, and execute the first query, wherein a first subset of theplurality of records is identified, the first subset of the plurality ofrecords corresponding to a treatment set of patients, each of whoserespective database records indicate a diagnosis of a brain conditionand that the respective patient in the treatment set received atreatment, receive a second input from a database user, convert thesecond input into a second query, and execute the second query, whereina second subset of the plurality of database records is identified, thesecond subset of the plurality of database records corresponding to aset of patients that did not receive the treatment, each of whoserespective database records indicate a diagnosis of the brain condition,output a first statistical comparison of the first subset of theplurality of database records to a normative set of electronic recordscorresponding to patients that were not diagnosed with the braincondition and did not receive the treatment rank a candidate subset ofthe patient database records according to relevancy to the set ofsymptoms of the patient, wherein a ranking is produced, and output inuser viewable form at least a portion of the ranking, wherein a possiblediagnosis of the clinical patient is provided.
 17. The system of claim16, wherein the one or more computer processors further: output a secondstatistical comparison of the second subset of the plurality of databaserecords to the normative set of electronic records, wherein informationregarding one or more of the basis of the brain condition andeffectiveness of the treatment for the brain condition are analyzedbased at least in part on the first statistical comparison and thesecond statistical comparison.
 18. The system of claim 16, wherein eachdigital representation of a baseline functional brain scan imagecomprises a set of location-specific values reflecting differences fromnormal.
 19. The system of claim 16, wherein the one or more computerprocessors further identify and output one or more features present inat least a percentage of a condition subset of the plurality of records.20. The system of claim 16, further comprising a nuclear imaging cameraconfigured to obtain a baseline brain scan of the person comprising aplurality of image planes and a concentration brain scan image of theperson comprising a plurality of image planes.
 21. The system of claim16, wherein the one or more computer processors further: receive aninput reflecting a set of symptoms of the person, convert the input intoa first query, and execute the first query, wherein a candidate subsetof the patient database records is identified, the candidate subset ofthe patient database records corresponding to a set of patients whoserespective patient database records include the set of symptoms of theperson, and cause the display of: at least one baseline brain scan imageand at least one concentration scan image, and at least one brain scanimage of the candidate subset of the patient database records.
 22. Thesystem of claim 16, wherein one or more computer processors further:receive an input from a database user, wherein the input comprises anidentifier for a first patient having an index file in the plurality ofrecords, and one or more search parameters, wherein the index filecomprises brain measurements compared to norm values, convert the inputinto a query, and execute the query, wherein a subset of the pluralityof records is identified, the subset of the plurality of recordscorresponding to a set of patients, each of whose respective databaserecords indicate an index file that substantially matches the firstpatient's index file based on the one or more search parameters,determine an average index file for the subset of the plurality ofrecords, and output a comparison of the index file of each patient inthe set of patients to the average index file.
 23. The system of claim22, wherein the index file comprises brain measurements compared to normvalues.
 24. The system of claim 16, wherein the one or more computerprocessors further: create an index file for a first patient having atleast one record in the plurality of records, wherein the index filecomprises brain measurements compared to norm values and wherein theindex file is created at least in part by determining digital levels ofstatistical deviation in brain measurement levels between the brainscans of the first patient and normative brain scans of patients with noindications of chronic or acute brain conditions, store the index filein the plurality of records, execute a query, where a subset of theplurality of records is identified, the subset of the plurality ofrecords corresponding to a set of patients, each of whose respectivedatabase records indicate an index file that substantially matches thefirst patient's index file based on one or more search parameters,determine an average index file for the subset of the plurality ofrecords, and output a comparison of the index file of each patient inthe set of patients to the average index file.
 25. The system of claim24, wherein the index file is a multi-dimensioned table or array. 26.The system of claim 25, wherein the index file is created using valuesrepresenting differences in brain measurement levels between the brainscans of the first patient and the normative brain scans.
 27. The systemof claim 24, wherein each digital representation of a baselinefunctional brain scan image comprises a set of location-specific valuesreflecting differences from normal.
 28. The system of claim 16, whereinone or more computer processors further: receive an input from adatabase user, wherein the input comprises an identifier for a firstpatient having an index file in the plurality of records, and one ormore search parameters wherein the index file comprises brainmeasurements compared to norm values.
 29. The system of claim 16,wherein one or more computer processors further: create an index filefor a first patient having at least one record in the plurality ofrecords, wherein the index file comprises brain measurements compared tonorm values and wherein the index file is created by determining digitallevels of statistical deviation in brain measurement levels between thebrain scans of the first patient and normative brain scans of patientswith no indications of chronic or acute brain conditions.
 30. The systemof claim 16, wherein each digital representation of a baselinefunctional brain scan image comprises a set of location-specific valuesreflecting differences from normal.
 31. A method comprising: providingan electronic database comprising a plurality of records, each of theplurality of records associated with a respective patient, each of theplurality of records comprising: one or more digital representations ofa baseline functional brain scan image comprising a plurality of imageplanes for a respective patient, a digital representation of a medicalhistory of a respective patient, a digital representation of demographicinformation of a respective patient, and a digital representation of aset of clinical symptoms of a respective patient; receiving a firstinput from a database user; converting the first input into a firstquery; executing the first query, wherein a first subset of theplurality of records is identified, the first subset of the plurality ofrecords corresponding to a treatment set of patients, each of whoserespective database records indicate a diagnosis of the brain conditionand that the respective patient in the treatment set received atreatment; receiving a second input from a database user; converting thesecond input into a second query; executing the second query, wherein asecond subset of the plurality of database records is identified, thesecond subset of the plurality of database records corresponding to aset of patients that did not receive the treatment, each of whoserespective database records indicate a diagnosis of the brain condition;outputting a first statistical comparison of the first subset of theplurality of database records to a normative set of electronic recordscorresponding to patients that were not diagnosed with the braincondition and did not receive the treatment wherein the database user ispresented with one or more features, a percentage of disease databaserecords in which the one or more features are present, and a percentageof normative database records in which the one or more features arepresent.
 32. The method of claim 31, wherein each digital representationof a baseline functional brain scan image comprises a set oflocation-specific values reflecting differences from normal.
 33. Amethod comprising: providing an electronic database comprising aplurality of records, each of the plurality of records associated with arespective patient, each of the plurality of records comprising: one ormore digital representations of a baseline functional brain scan imagecomprising a plurality of image planes for a respective patient, adigital representation of a medical history of a respective patient, adigital representation of demographic information of a respectivepatient, and a digital representation of a set of clinical symptoms of arespective patient; receiving a first input from a database user;converting the first input into a first query; executing the firstquery, wherein a first subset of the plurality of records is identified,the first subset of the plurality of records corresponding to atreatment set of patients, each of whose respective database recordsindicate a diagnosis of the brain condition and that the respectivepatient in the treatment set received a treatment; receiving a secondinput from a database user; converting the second input into a secondquery; executing the second query, wherein a second subset of theplurality of database records is identified, the second subset of theplurality of database records corresponding to a set of patients thatdid not receive the treatment, each of whose respective database recordsindicate a diagnosis of the brain condition; outputting a firststatistical comparison of the first subset of the plurality of databaserecords to a nonnative set of electronic records corresponding topatients that were not diagnosed with the brain condition and did notreceive the treatment; ranking a candidate subset of the plurality ofrecords according to relevancy to a set of symptoms of the respectivepatient, wherein a ranking is produced; and outputting in a userviewable form at least a portion of the ranking and a possible diagnosisof the respective patient.
 34. The method of claim 33, wherein eachdigital representation of a baseline functional brain scan imagecomprises a set of location-specific values reflecting differences fromnormal.